Reading and writing data

ANU BDSI
workshop Introduction to R programming

Emi Tanaka

Biological Data Science Institute

3rd April 2024

Current learning objective

-Conduct elementary arithmetic operations using R
-Navigate the RStudio interactive development environment (IDE)
-Compute basic summary statistics including mean, median, quartiles, and standard deviation using R
-Install external packages in R to extend functionality
-Comprehend various object types in R
-Manipulate lists, matrices, and vectors in R
-Grasp the concept of missing values within the R environment
Import and export data in R
-Create basic functions, employ conditional statements, and utilize for loops in R
-Decipher error messages and do basic troubleshooting

Data are stored as a file, which points to a block of computer memory.
A file format signals a way to interpret the information stored in the computer memory.
A file with the extension “csv” (comma-separated values) uses a comma as a delimiter while “tsv” uses tabs as a delimiter.

data.csv

len, supp, dose
4.2, VC, 0.5
11.5, VC, 0.5
...

Data can also be stored as a binary format (in R, these include .RData and .rda but we won’t cover this).
Data can also come in a propriety format (e.g. xls and xlsx) – these require special ways to open/view/read it.

Unless you are responsible for entering the data, you should never modify the original, stored data (note: exceptions do apply).
For scientific integrity, any modification to the original data should be recorded in a reproducible manner (e.g. by programming in R!) so that you can trace the exact modifications.

Reading data into R requires using the function that matches with the right file format.

For reading csv file, you can use readr::read_csv() (note: readr is part of tidyverse)

cfdata <- readr::read_csv("complex-formation.csv")

kingston <- readxl::read_xlsx("kingston-data.xlsx", sheet = "A")

Error: `path` does not exist: 'kingston-data.xlsx'

Your file has to be in the right location to be read!
You may use a relative path (e.g. data/data.csv) or an absolute path (e.g. C:\\user/myproject/data/data.csv) to point to the right location of the data
You should avoid using absolute path! Why?
You can get and set the current path using getwd() and setwd(), respectively.

Your folder structure depends on the project, but it is generally a good idea to have a folder on its own for each project.

Within the project, it is also good to have a separate folder for:
- data
- script/analysis
- report/paper
- figures/images.

Within RStudio, you can create a project file (with an .Rproj extension).
Double clicking on this project file launches RStudio Desktop with the current working directory set to the location of the project file.
You can create this project file by going to RStudio > File > New Project …